code: def forward(self, input):
      dense_input = input[:, :-2]
      month = input[:, -2].long()
      hour = input[:, -1].long()
  
      month_emb = self.month_embedding(month)
      hour_emb = self.hour_embedding(hour)
  
      output = torch.cat((dense_input, month_emb, hour_emb), dim=1)
  
      output = self.linear_1(output)
      output = self.linear_2(output)
      output = self.linear_3(output)
      output = self.linear_4(output)
      output = self.linear_5(output)
      return self.linear_6(output)
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track1/powernet_model.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the UnitaryActionModel obj's underlying member method code with example usage: obj.forward(input) and returns: self